Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Regression Analysis01:11

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Features predicting data exclusion in imaging studies of Alzheimer's disease.

Alzheimer's & dementia (Amsterdam, Netherlands)·2026
Same author

The Prodromal Synucleinopathy Rating Scale: An Assessment in Patients With REM Sleep Behavior Disorder.

Neurology·2026
Same author

Childhood neurodevelopmental outcomes following hypertensive disorders of pregnancy.

Pregnancy hypertension·2026
Same author

Weight Change Between Pregnancies and Mortality Over 50 Years of Follow-Up.

Obesity (Silver Spring, Md.)·2026
Same author

Determination of the Latency Period Between Weekly Gestational Weight Gain and Fetal Growth.

Paediatric and perinatal epidemiology·2026
Same author

Effect of Cognitive Reserve on Age at Symptom Onset and Cognitive Decline in Individuals With Dominantly Inherited Alzheimer Disease.

Neurology·2026

Related Experiment Video

Updated: Jun 6, 2026

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

Predicting birth weight by multivariate functional principal component regressions.

Yaeji Lim1, Ruijin Lu2, Madeleine St Ville3

  • 1Department of Applied Statistics, Chung-Ang University, Seoul, South Korea.

The International Journal of Biostatistics
|June 4, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model for predicting birth weight using maternal diet data. The multivariate functional principal component regression (MVFPCR) model effectively analyzes dietary patterns to forecast infant birth weight.

Keywords:
alternative healthy eating indexbirth weight predictionfunctional predictor regressionfunctional principal component analysismultivariate functional data analysis

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Related Experiment Videos

Last Updated: Jun 6, 2026

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Functional data analysis (FDA) offers advanced statistical methods for complex, high-dimensional data like curves or functions.
  • Predicting birth weight is crucial for fetal health and requires sophisticated analytical tools for intricate data like maternal dietary patterns.

Purpose of the Study:

  • To develop and validate a novel multivariate functional principal component regression (MVFPCR) model for predicting birth weight.
  • To utilize maternal dietary patterns, specifically trajectories of the Alternative Healthy Eating Index (AHEI) components, as functional predictors.

Main Methods:

  • Employed Multivariate Functional Principal Component Analysis (MVFPCA) to reduce dimensionality and decorrelate multivariate functional predictors.
  • Developed a novel MVFPCR model integrating MVFPCA scores for scalar-on-function regression.
  • Incorporated flexible regression techniques, including linear and quantile regression, to accommodate varying birth weight distributions.

Main Results:

  • Simulation studies confirmed the model's effectiveness in functional predictor regression.
  • Application to a fetal growth study dataset demonstrated the MVFPCR model's capability in predicting birth weight from maternal dietary data.
  • The MVFPCA approach successfully generated low-dimensional representations of complex dietary patterns.

Conclusions:

  • The proposed MVFPCR model provides an effective framework for predicting birth weight using multivariate functional predictors derived from maternal dietary patterns.
  • This approach enhances the analysis of complex functional data in epidemiological and biostatistical research.
  • The model's flexibility allows for tailored predictions based on the characteristics of the outcome variable.